Virus detection using nanoparticles and deep neural network–enabled smartphone system
| Autor: | Anish Vasan, Raymond T. Chung, Sanchana Krishnakumar, Manoj Kumar Kanakasabapathy, Aradana Muthupandian, Xu G. Yu, Hadi Shafiee, Prudhvi Thirumalaraju, Mohamed Shehata Draz, Aparna Sreeram, V. Yogesh, Wenyu Lin |
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| Rok vydání: | 2020 |
| Předmět: |
Gas bubble
Hepatitis B virus Multidisciplinary Artificial neural network biology Computer science viruses ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION SciAdv r-articles Serum samples medicine.disease_cause biology.organism_classification Virology Virus Zika virus Virus detection Applied Sciences and Engineering Visual patterns medicine Health and Medicine Research Articles Research Article |
| Zdroj: | Science Advances |
| ISSN: | 2375-2548 |
| DOI: | 10.1126/sciadv.abd5354 |
| Popis: | A virus detection method using deep learning–based analysis of smartphone-recorded microchip images without any optical hardware. Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)–enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%. |
| Databáze: | OpenAIRE |
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